This file describes the main tutorial PDF file. Almost all tutorials and hands-on practices are indeed collated in a single document. In addition to this PDF, R code excerpts and installation instructions are also provided.

R is a free, open-source software environment and programming language for statistical computing and graphics. It is available for all computer platforms and is widely used, and many packages have been developed in the Bioconductor project for analysis of genomic data. This module covers the...

Keywords: Prerequisite, R-programming

Intro to R and Bioconductorhttps://microasp.upsc.se/ngs_trainers/Materials/tree/master/Content/Prerequisite/Jenny_Drnevich/README.mdhttps://tess.elixir-europe.org/materials/intro-to-r-and-bioconductorR is a free, open-source software environment and programming language for statistical computing and graphics. It is available for all computer platforms and is widely used, and many packages have been developed in the Bioconductor project for analysis of genomic data. This module covers the basic skills that will be needed before using R to analyze NGS data.Jenny Drnevich @jennyPrerequisite, R-programming

Material for the course held on EBI Campus, Welcome Trust Center, Hinxton, UK on 20-26th, October 2014. The material cover general RNA-Seq data pre-processing as described in these [guidelines](http://www.epigenesys.eu/en/protocols/bio-informatics/1283-guidelines-for-rna-seq-data-analysis) and...

Differential expression analysis on the Robinson, Delhomme et al. dataset.https://microasp.upsc.se/ngs_trainers/Materials/tree/master/Content/RNA-Seq/Nicolas_Delhomme/EMBO-Oct-2014/06_EMBO-October-2014-Differential-expression.mdhttps://tess.elixir-europe.org/materials/differential-expression-analysis-on-the-robinson-delhomme-et-al-datasetA differential expression analysis conducted on the **[Robinson, Delhomme et al., dataset](https://microasp.upsc.se/ngs_trainers/Materials/blob/master/Datasets/Robinson-Delhomme-Populus-tremula-shows-no-evidence-of-sexual-dimorphism.md)**. The dataset has 17 samples and 2 important meta-data: the sample sex and year of collection. The goal is to test whether genes are involved in different processes based on the sex of the tree; _i.e._ is there a sexual dimorphism in _Populus tremula_ trees. It has indeed been hypothesized that male tree should be taller so as to spread their pollen further, whereas female would be more resistant to pests and diseases. The existing literature is contradictory, however it resulted from studies where plants were grown in controlled environment. In the present dataset, plant samples were collected in the wild, at a 2 years interval. The latter is a very important factor in the analysis as the 'year effect' is a strong confounding factor that hides the 'sex effect'. The present tutorial, hence, introduces a differential-expression analysis, but goes further by adressing confounding factors and how to _block_ them in an analysis. It is a good dataset to remind trainees that they should always be critical towards the conclusion they draw from their data.Bastian Schiffthaler @bastianNicolas Delhomme @delhommeRNA-SeqRNA-Seq, Differential-expression, R-programming, Statistical-model

This lecture gives an overview of exploratory analysis (clustering) and supervised analysis (prediction/classification), as well as visualization methods (heatmaps/PCA) and gene set analysis. It also shows how to transform count data to make it more suitable to apply the traditional methods...

Keywords: Statistical-model, Exploratory-analysis

Exploratory analysis and downstream analysishttps://microasp.upsc.se/ngs_trainers/Materials/tree/master/Content/RNA-Seq/Charlotte_Soneson/downstream_analysis_lecture_2015.mdhttps://tess.elixir-europe.org/materials/exploratory-analysis-and-downstream-analysisThis lecture gives an overview of exploratory analysis (clustering) and supervised analysis (prediction/classification), as well as visualization methods (heatmaps/PCA) and gene set analysis. It also shows how to transform count data to make it more suitable to apply the traditional methods developed (e.g.) for microarray data.Charlotte Soneson @charlotte, charlottesoneson@gmail.comStatistical-model, Exploratory-analysis